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Home » Alphabet stock falls on reports of Gemini 3.5 Pro AI model delay
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Alphabet stock falls on reports of Gemini 3.5 Pro AI model delay

Editor-In-ChiefBy Editor-In-ChiefJuly 16, 2026No Comments2 Mins Read
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alphabet Shares fell 4% on Thursday after reports that the company had postponed the launch of its flagship artificial intelligence model.

The search giant’s Gemini 3.5 Pro AI model is several months behind schedule due to the company’s efforts to improve performance, Bloomberg reported, citing sources familiar with the matter. In particular, the model’s coding capabilities fell short of internal expectations, and it arrived at a time when rivals were performing well. OpenAI and Meta The company recently announced a new AI model that outperforms Google’s current offering at generating software code, according to the report.

The company previously announced the Gemini 3.5 Pro AI model in May as part of the annual Google I/O developer conference, saying at the time that it was being used internally but wouldn’t be ready for widespread deployment until next month.

An Alphabet spokesperson told CNBC in an emailed statement that the company “ships a wide range of models quickly while remaining cost-effective for our customers.”

“We are currently working with our partners to test 3.5 Pro, the upgraded Flash model, and other models, and are working productively with the U.S. government,” the spokesperson said.

Code generation has become one of the biggest use cases for AI model providers, including: human OpenAI, Chinese AI Lab, etc. Zai We offer so-called openweight variants that developers can access for free via the open source ecosystem.

Meta debuted the Muse Spark 1.1 AI model last week. Alexandr Wang, the company’s head of AI, described it as the social media giant’s “most powerful model yet for agent and coding work.”

OpenAI released its GPT-5.6 Sol AI model last week, and CEO Sam Altman said the model is 54% more token efficient on agent coding tasks, highlighting that the AI ​​Lab touts each AI coding model as cost-effective relative to performance.

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